ISOM-R1-Enterprise-40B: 40B System-2 Deliberation Foundation

DOI LinkedIn Base Context Params Space


Overview

ISOM-R1-Enterprise-40B brings bounded-state memory and continuous isometric operator manifolds to 40-billion parameter foundation scale. Designed for enterprise deliberation and long document analysis, it addresses the memory constraints of deep 60-layer multi-query attention architectures.


Model Primary Architecture Role Base Lineage (Independent Derivative) Total / Active Parameters Max Context Cache Complexity Hardware Target
ISOM-R1-Coder-16B-MoE 160K Bounded Code & MLA MoE DeepSeek-Coder-V2-Lite (Non-Endorsed) 15.71B / 2.36B Active 163,840 (160K) O(1) Bounded Manifold (Architectural Spec) 16GB Cloud / Multi-GPU
ISOM-R1-Enterprise-40B 40B System-2 Foundation Reasoning Falcon-40B (Non-Endorsed) 40.0B Dense 32,768 (32K) O(1) Bounded State (Architectural Spec) Enterprise Multi-GPU (24GB-80GB)
ISOM-R1-Coder-1.5B-Instruct 128K Repository Code Intelligence Qwen2.5-Coder-1.5B-Instruct (Non-Endorsed) 1.54B Dense 131,072 (128K) O(1) Bounded State (Tesla T4 Verified) 8GB Developer Laptops / Edge
ISOM-R1-Reasoning-1.5B-Instruct 32K System-2 Mathematical Deliberation Qwen2.5-1.5B-Instruct (Non-Endorsed) 1.54B Dense 32,768 (32K) O(1) Bounded State (Tesla T4 Verified) 8GB Edge / Consumer GPUs
ISOM-R1-Edge-130M-MoE Unbounded Recurrent Drafter & SSM Standalone Continuous SSM + MoE 134.89M / 58.27M Active Unbounded Recurrence O(1) Recurrent State (0.0469 MB Verified) Ultra-Low Power Edge & CPU

Theoretical Architectural Specifications

Metric Specification
Total Parameters 40.0 Billion Dense
Layers 60 Deep Decoder Layers
Attention Architecture Multi-Query Attention (MQA, 8 KV heads)
Base Model tiiuae/falcon-40b-instruct (Non-Endorsed)
Context Window 32,768 tokens (32K)
Working Memory Complexity O(1) Bounded State (Architectural Spec)
Target Hardware Enterprise Multi-GPU (24GB-80GB)

Theoretical MQA Tensor Geometry & Memory Bounds

In standard Multi-Query Attention (MQA) across 60 layers:

Context Length Standard MQA Attention KV (FP16) Standard MQA Attention KV (INT8) ISOM Theoretical State Spec (INT8)
4,096 tokens 0.49 GB 0.25 GB 0.25 GB
8,192 tokens 0.98 GB 0.49 GB 0.49 GB
16,384 tokens 1.97 GB 0.98 GB 0.49 GB
32,768 tokens 3.93 GB 1.97 GB 0.49 GB

Architectural Specification Notice: Values above represent theoretical dimensional derivations based on Falcon-40B Multi-Query Attention (MQA) tensor geometries. Empirical validation across 32K sequences requires enterprise multi-GPU hardware (24GB-80GB) and is not claimed as an audited hardware measurement.


Quickstart & Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Prannesshkva/ISOM-R1-Enterprise-40B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "Analyze the stability of geodesic flows on compact Lie groups under perturbed curvature tensors."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        temperature=0.7
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation & Licensing

@article{prannessh2026isom40b,
  title={ISOM-R1-Enterprise-40B: Bounded-Memory Multi-Query Attention at 40B Scale},
  author={Prannessh K.V.A.},
  journal={CERN Zenodo},
  year={2026},
  doi={10.5281/zenodo.22649142},
  url={https://doi.org/10.5281/zenodo.22649142}
}
  • Sole Author & Architect: Prannessh K.V.A.
  • LinkedIn: Prannessh K.V.A.
  • License: Released under CC BY-NC-ND 4.0 / BSL 1.1 for research and evaluation. Commercial production licenses available via LinkedIn inquiry.


Notice of Non-Endorsement & Independent Lineage

Independent Derivative Work: ISOM-R1-Enterprise-40B is an independent research implementation engineered solely by Prannessh K.V.A. (Author & Architect). It builds upon tiiuae/falcon-40b-instruct under the Apache 2.0 License. This release is not affiliated with, endorsed by, or sponsored by the Technology Innovation Institute (TII). All continuous isometric state operator manifolds, Cayley SO(d) projection operators, and memory-bounding integrations are proprietary research contributions of the author.

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